From 96cd9991c25f50bc11cc8a7a04b556316cdbf8e7 Mon Sep 17 00:00:00 2001 From: MillyWei Date: Tue, 5 Aug 2025 13:42:43 +0800 Subject: [PATCH] Add BAAI/bge-small-en-v1.5 --- README.md | 2 +- baai-bge-small-en-v1.5/aitk/.gitignore | 6 + baai-bge-small-en-v1.5/aitk/README.md | 165 +++++++++++ .../bge-small-en-v1.5_context_ov_static.json | 203 +++++++++++++ ...mall-en-v1.5_context_ov_static.json.config | 182 ++++++++++++ .../aitk/bge-small-en-v1.5_qdq_amd.json | 230 +++++++++++++++ .../bge-small-en-v1.5_qdq_amd.json.config | 273 ++++++++++++++++++ .../aitk/bge-small-en-v1.5_qdq_qnn.json | 205 +++++++++++++ .../bge-small-en-v1.5_qdq_qnn.json.config | 231 +++++++++++++++ .../aitk/inference_sample.ipynb | 150 ++++++++++ baai-bge-small-en-v1.5/aitk/info.yml | 20 ++ .../aitk/model_project.config | 20 ++ baai-bge-small-en-v1.5/aitk/requirements.txt | 5 + baai-bge-small-en-v1.5/aitk/user_script.py | 143 +++++++++ 14 files changed, 1834 insertions(+), 1 deletion(-) create mode 100644 baai-bge-small-en-v1.5/aitk/.gitignore create mode 100644 baai-bge-small-en-v1.5/aitk/README.md create mode 100644 baai-bge-small-en-v1.5/aitk/bge-small-en-v1.5_context_ov_static.json create mode 100644 baai-bge-small-en-v1.5/aitk/bge-small-en-v1.5_context_ov_static.json.config create mode 100644 baai-bge-small-en-v1.5/aitk/bge-small-en-v1.5_qdq_amd.json create mode 100644 baai-bge-small-en-v1.5/aitk/bge-small-en-v1.5_qdq_amd.json.config create mode 100644 baai-bge-small-en-v1.5/aitk/bge-small-en-v1.5_qdq_qnn.json create mode 100644 baai-bge-small-en-v1.5/aitk/bge-small-en-v1.5_qdq_qnn.json.config create mode 100644 baai-bge-small-en-v1.5/aitk/inference_sample.ipynb create mode 100644 baai-bge-small-en-v1.5/aitk/info.yml create mode 100644 baai-bge-small-en-v1.5/aitk/model_project.config create mode 100644 baai-bge-small-en-v1.5/aitk/requirements.txt create mode 100644 baai-bge-small-en-v1.5/aitk/user_script.py diff --git a/README.md b/README.md index 53c5d84e..a97838f0 100644 --- a/README.md +++ b/README.md @@ -21,7 +21,7 @@ Below are list of available recipes grouped by different criteria. Click the lin | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | [google-bert-bert-base-multilingual-cased](google-bert-bert-base-multilingual-cased/aitk) | [laion-CLIP-ViT-B-32-laion2B-s34B-b79K](laion-CLIP-ViT-B-32-laion2B-s34B-b79K/aitk) | [deepseek-ai-DeepSeek-R1-Distill-Qwen-1.5B](deepseek-ai-DeepSeek-R1-Distill-Qwen-1.5B/aitk) | [meta-llama-Llama-3.2-1B-Instruct](meta-llama-Llama-3.2-1B-Instruct/NvTensorRtRtx) | [mistralai-Mistral-7B-Instruct-v0.3](mistralai-Mistral-7B-Instruct-v0.3/aitk) | [microsoft-Phi-3.5-mini-instruct](microsoft-Phi-3.5-mini-instruct/aitk) | [microsoft-Phi-3.5-mini-instruct](microsoft-Phi-3.5-mini-instruct/NvTensorRtRtx) | [Qwen-Qwen2.5-1.5B-Instruct](Qwen-Qwen2.5-1.5B-Instruct/NvTensorRtRtx) | [microsoft-resnet-50](microsoft-resnet-50/aitk) | [google-vit-base-patch16-224](google-vit-base-patch16-224/aitk) | | [intel-bert-base-uncased-mrpc](intel-bert-base-uncased-mrpc/aitk) | [openai-clip-vit-base-patch16](openai-clip-vit-base-patch16/aitk) | | [meta-llama-Llama-3.2-1B-Instruct](meta-llama-Llama-3.2-1B-Instruct/aitk) | | [microsoft-Phi-4-mini-reasoning](microsoft-Phi-4-mini-reasoning/aitk) | | [Qwen-Qwen2.5-1.5B-Instruct](Qwen-Qwen2.5-1.5B-Instruct/aitk) | | | -| | [openai-clip-vit-base-patch32](openai-clip-vit-base-patch32/aitk) | | | | | | [deepseek-ai-DeepSeek-R1-Distill-Qwen-1.5B](deepseek-ai-DeepSeek-R1-Distill-Qwen-1.5B/NvTensorRtRtx) | | | +|[BAAI/bge-small-en-v1.5](baai-bge-small-en-v1.5/aitk)| [openai-clip-vit-base-patch32](openai-clip-vit-base-patch32/aitk) | | | | | | [deepseek-ai-DeepSeek-R1-Distill-Qwen-1.5B](deepseek-ai-DeepSeek-R1-Distill-Qwen-1.5B/NvTensorRtRtx) | | | diff --git a/baai-bge-small-en-v1.5/aitk/.gitignore b/baai-bge-small-en-v1.5/aitk/.gitignore new file mode 100644 index 00000000..f2371b12 --- /dev/null +++ b/baai-bge-small-en-v1.5/aitk/.gitignore @@ -0,0 +1,6 @@ +__pycache__ +/cache +/history/*/* +!/history/*/history.config +!/history/*/olive_config.json +.DS_Store diff --git a/baai-bge-small-en-v1.5/aitk/README.md b/baai-bge-small-en-v1.5/aitk/README.md new file mode 100644 index 00000000..f76ded4a --- /dev/null +++ b/baai-bge-small-en-v1.5/aitk/README.md @@ -0,0 +1,165 @@ +# BGE-Small-EN-v1.5 Optimization + +This folder contains examples of BGE-Small-EN-v1.5 optimization using different workflows for various hardware accelerators. + +## Model Overview + +BGE-Small-EN-v1.5 is a lightweight English text embedding model developed by BAAI (Beijing Academy of Artificial Intelligence). The model is optimized for sentence and text embedding tasks, providing high-quality vector representations for downstream applications such as semantic search, text classification, and similarity matching. + +## Optimization Workflows + +This directory provides three different optimization workflows targeting specific hardware accelerators: + +- **QDQ for Qualcomm NPU**: Quantization-aware training for Qualcomm Neural Processing Units +- **QDQ for AMD NPU**: Quantization-aware training for AMD Neural Processing Units +- **OpenVINO for Intel NPU**: OpenVINO optimization for Intel Neural Processing Units + +## Workflow Details + +### QDQ for Qualcomm NPU + +This workflow performs quantization-aware training optimization for Qualcomm NPU acceleration. It follows the optimization pipeline: + +- *HuggingFace Model → ONNX Model → Quantized ONNX Model* + +**Configuration File**: `bge-small-en-v1.5_qdq_qnn.json` + +**Key Features**: +- Uses QNN (Qualcomm Neural Network) execution provider +- Implements quantization-aware training with dynamic quantization +- Optimized for Qualcomm NPU hardware architecture +- Supports both activation and weight quantization + +### QDQ for AMD NPU + +This workflow performs quantization-aware training optimization for AMD NPU acceleration. It follows the optimization pipeline: + +- *HuggingFace Model → ONNX Model → Quantized ONNX Model* + +**Configuration File**: `bge-small-en-v1.5_qdq_amd.json` + +**Key Features**: +- Optimized for AMD NPU architecture +- Implements quantization-aware training with dynamic quantization +- Enhanced performance for AMD hardware +- Supports both activation and weight quantization + +### OpenVINO for Intel NPU + +This workflow performs OpenVINO optimization for Intel NPU acceleration. It follows the optimization pipeline: + +- *HuggingFace Model → OpenVINO IR Model* + +**Configuration File**: `bge-small-en-v1.5_context_ov_static.json` + +**Key Features**: +- Uses OpenVINO execution provider for Intel NPU +- Implements static quantization for optimal performance +- Custom user script for specialized data processing +- Enhanced accuracy evaluation using MTEB benchmarks + +## Dataset Information + +### Quantization Datasets +- **QNN/AMD NPU**: Uses MTEB Banking77 test split for quantization calibration +- **Intel NPU**: Uses Wikipedia train split (300 samples) with custom preprocessing + +### Evaluation Datasets +- **Primary**: MTEB Banking77 classification task +- **Evaluation Metric**: Custom embedding accuracy for semantic similarity +- **Benchmark**: MTEB (Massive Text Embedding Benchmark) for standardized evaluation + +## Performance Evaluation Results + +The following results are based on comprehensive evaluation using standard embedding benchmarks and performance metrics. All evaluations use the MTEB Banking77 dataset for consistency. + +### Qualcomm NPU (QNN) Performance + +| Metric | Value | +|--------|-------| +| **Accuracy** | 85.57% | +| **Latency (avg)** | 14.83 ms | +| **Latency (min)** | 13.66 ms | +| **Latency (max)** | 17.92 ms | +| **Latency (p90)** | 15.52 ms | +| **Throughput (avg)** | 70.97 tokens/sec | +| **Throughput (max)** | 72.83 tokens/sec | +| **Throughput (min)** | 68.47 tokens/sec | + +### AMD NPU Performance + +| Metric | Value | +|--------|-------| +| **Accuracy** | 83.66% | +| **Latency (avg)** | 8.58 ms | +| **Latency (min)** | 7.54 ms | +| **Latency (max)** | 9.43 ms | +| **Latency (p90)** | 9.13 ms | +| **Throughput (avg)** | 107.26 tokens/sec | +| **Throughput (max)** | 130.15 tokens/sec | +| **Throughput (min)** | 88.90 tokens/sec | + +### Intel NPU Performance + +| Metric | Value | +|--------|-------| +| **Accuracy** | 85.42% | +| **Latency (avg)** | 3.33 ms | +| **Latency (min)** | 2.30 ms | +| **Latency (max)** | 6.39 ms | +| **Latency (p90)** | 4.01 ms | +| **Throughput (avg)** | 312.15 tokens/sec | +| **Throughput (max)** | 421.12 tokens/sec | +| **Throughput (min)** | 199.13 tokens/sec | + +## Optimization Techniques + +### Quantization Strategies +- **Dynamic Quantization**: Used for QNN and AMD NPU workflows +- **Static Quantization**: Used for Intel NPU workflow with OpenVINO +- **Mixed Precision**: Combines different precision levels for optimal performance + +### Model Optimization Features +- **Input Optimization**: Fixed input shapes for better inference performance +- **Memory Optimization**: Efficient memory usage through quantization +- **Hardware-Specific Tuning**: Custom optimizations for each NPU architecture + +## Requirements + +The following dependencies are required for running the optimization workflows: + +``` +olive-ai +datasets +optimum +mteb +polars-lts-cpu (QNN only) +``` + +## Usage + +1. **Select Workflow**: Choose the appropriate configuration file based on your target hardware: + - For Qualcomm NPU: `bge-small-en-v1.5_qdq_qnn.json` + - For AMD NPU: `bge-small-en-v1.5_qdq_amd.json` + - For Intel NPU: `bge-small-en-v1.5_context_ov_static.json` + +2. **Configure Parameters**: Adjust quantization parameters such as activation type, weight type, and quantization dataset according to your specific requirements. + +3. **Run Optimization**: Execute the optimization pipeline using the selected configuration. + +4. **Evaluate Results**: Use the provided evaluation scripts to assess model performance on your target hardware. + +## Performance Notes + +- **Accuracy**: Measured using custom embedding accuracy metrics from MTEB benchmark +- **Latency**: Measured in milliseconds per inference +- **Throughput**: Measured in tokens per second +- +## Model Information + +- **Model ID**: `BAAI/bge-small-en-v1.5` +- **Model Type**: Text Embedding Model +- **Framework**: HuggingFace Transformers +- **Optimization Target**: Hardware-specific acceleration for embedding generation + +*Note: Performance metrics may vary depending on hardware specifications, system environment, and workload characteristics. The values provided here are for reference and may not reflect performance on all devices or configurations.* \ No newline at end of file diff --git a/baai-bge-small-en-v1.5/aitk/bge-small-en-v1.5_context_ov_static.json b/baai-bge-small-en-v1.5/aitk/bge-small-en-v1.5_context_ov_static.json new file mode 100644 index 00000000..6ed5c6d0 --- /dev/null +++ b/baai-bge-small-en-v1.5/aitk/bge-small-en-v1.5_context_ov_static.json @@ -0,0 +1,203 @@ +{ + "input_model": { + "type": "HfModel", + "model_path": "BAAI/bge-small-en-v1.5", + "task": "feature-extraction", + "io_config": { + "input_names": [ + "input_ids", + "attention_mask", + "token_type_ids" + ], + "input_shapes": [ + [ + 1, + 128 + ], + [ + 1, + 128 + ], + [ + 1, + 128 + ] + ], + "input_types": [ + "int64", + "int64", + "int64" + ], + "output_names": [ + "last_hidden_state", + "state" + ] + } + }, + "systems": { + "local_system": { + "type": "LocalSystem", + "accelerators": [ + { + "device": "npu", + "execution_providers": [ + "OpenVINOExecutionProvider" + ] + } + ] + } + }, + "data_configs": [ + { + "name": "quantize_data_config", + "user_script": "user_script.py", + "load_dataset_config": { + "type": "bge_small_en_dataset", + "data_name": "wikipedia", + "split": "train", + "max_samples": 300 + }, + "dataloader_config": { + "batch_size": 1, + "drop_last": true + } + }, + { + "name": "accuracy_data_config", + "type": "HuggingfaceContainer", + "load_dataset_config": { + "data_name": "mteb/banking77", + "split": "test" + }, + "pre_process_data_config": { + "max_length": 128, + "padding": "max_length", + "input_cols": ["text"] + }, + "dataloader_config": { + "batch_size": 1 + } + }, + { + "name": "evaluation_data_config", + "type": "HuggingfaceContainer", + "load_dataset_config": { + "data_name": "mteb/banking77", + "split": "test" + }, + "pre_process_data_config": { + "max_length": 128, + "padding": "max_length", + "input_cols": ["text"] + }, + "dataloader_config": { + "batch_size": 1 + } + } + ], + "evaluators": { + "common_evaluator": { + "metrics": [ + { + "name": "accuracy", + "type": "custom", + "sub_types": [ + { + "name": "embedding_accuracy", + "priority": 1, + "higher_is_better": true, + "goal": { "type": "max-degradation", "value": 0.05 } + } + ], + "user_config": { + "user_script": "user_script.py", + "evaluate_func": "eval_accuracy" + } + }, + { + "name": "latency", + "type": "latency", + "data_config": "evaluation_data_config", + "sub_types": [ + { "name": "avg", "priority": 2, "metric_config": { "warmup_num": 20, "repeat_test_num": 100 } }, + { "name": "p50", "metric_config": { "warmup_num": 20, "repeat_test_num": 100 } }, + { "name": "p75", "metric_config": { "warmup_num": 20, "repeat_test_num": 100 } }, + { "name": "p90", "metric_config": { "warmup_num": 20, "repeat_test_num": 100 } }, + { "name": "p95", "metric_config": { "warmup_num": 20, "repeat_test_num": 100 } }, + { "name": "p99", "metric_config": { "warmup_num": 20, "repeat_test_num": 100 } }, + { "name": "min", "metric_config": { "warmup_num": 20, "repeat_test_num": 100 } }, + { "name": "max", "metric_config": { "warmup_num": 20, "repeat_test_num": 100 } } + ] + }, + { + "name": "throughput", + "type": "throughput", + "data_config": "evaluation_data_config", + "sub_types": [ + { "name": "avg", "priority": 3, "metric_config": { "warmup_num": 20, "repeat_test_num": 100 } }, + { "name": "p50", "metric_config": { "warmup_num": 20, "repeat_test_num": 100 } }, + { "name": "p75", "metric_config": { "warmup_num": 20, "repeat_test_num": 100 } }, + { "name": "p90", "metric_config": { "warmup_num": 20, "repeat_test_num": 100 } }, + { "name": "p95", "metric_config": { "warmup_num": 20, "repeat_test_num": 100 } }, + { "name": "p99", "metric_config": { "warmup_num": 20, "repeat_test_num": 100 } }, + { "name": "min", "metric_config": { "warmup_num": 20, "repeat_test_num": 100 } }, + { "name": "max", "metric_config": { "warmup_num": 20, "repeat_test_num": 100 } } + ] + } + ] + } + }, + "passes": { + "optimum_convert": { + "type": "OpenVINOOptimumConversion", + "extra_args": { + "device": "npu", + "task": "feature-extraction" + } + }, + "io_update": { + "type": "OpenVINOIoUpdate", + "input_shapes": [ + [ + 1, + 128 + ], + [ + 1, + 128 + ], + [ + 1, + 128 + ] + ], + "static": true + }, + "ov_quantize": { + "type": "OpenVINOQuantization", + "target_device": "npu", + "data_config": "quantize_data_config", + "model_type": "TRANSFORMER", + "user_script": "user_script.py", + "transform_fn": "custom_transform_func", + "extra_configs": [ + { + "advanced_quantization_parameters": { + "smooth_quant_alpha": 0.6 + } + } + ] + }, + "encapsulation": { + "type": "OpenVINOEncapsulation", + "target_device": "npu", + "ov_version": "2025.1" + } + }, + "cache_dir": "cache", + "evaluate_input_model": false, + "evaluator": "common_evaluator", + "host": "local_system", + "output_dir": "models/bge-small-en-v1.5/openvino", + "target": "local_system" +} \ No newline at end of file diff --git a/baai-bge-small-en-v1.5/aitk/bge-small-en-v1.5_context_ov_static.json.config b/baai-bge-small-en-v1.5/aitk/bge-small-en-v1.5_context_ov_static.json.config new file mode 100644 index 00000000..d7134549 --- /dev/null +++ b/baai-bge-small-en-v1.5/aitk/bge-small-en-v1.5_context_ov_static.json.config @@ -0,0 +1,182 @@ +{ + "name": "Convert to Intel CPU/NPU/GPU", + "oliveFile": "bge/bge-small-en-v1.5_ptq_qnn.json", + "isIntel": true, + "debugInfo": { + "autoGenerated": true, + "useOpenVINOOptimumConversion": "optimum_convert" + }, + "addCpu": false, + "runtime": { + "autoGenerated": true, + "name": "Evaluate on", + "type": "enum", + "displayNames": [ + "Intel CPU", + "Intel GPU", + "Intel NPU" + ], + "path": "systems.local_system.accelerators.0.device", + "values": [ + "cpu", + "gpu", + "npu" + ], + "readOnly": false + }, + "runtimeInConversion": { + "autoGenerated": true, + "name": "Convert/Quantize to", + "type": "enum", + "displayNames": [ + "Intel CPU", + "Intel GPU", + "Intel NPU" + ], + "path": "passes.optimum_convert.extra_args.device", + "values": [ + "cpu", + "gpu", + "npu" + ], + "actions": [ + [ + { + "type": "update", + "path": "passes.ov_quantize.target_device", + "value": "cpu" + }, + { + "type": "update", + "path": "passes.encapsulation.target_device", + "value": "cpu" + } + ], + [ + { + "type": "update", + "path": "passes.ov_quantize.target_device", + "value": "gpu" + }, + { + "type": "update", + "path": "passes.encapsulation.target_device", + "value": "gpu" + } + ], + [ + { + "type": "update", + "path": "passes.ov_quantize.target_device", + "value": "npu" + }, + { + "type": "update", + "path": "passes.encapsulation.target_device", + "value": "npu" + } + ] + ] + }, + "sections": [ + { + "autoGenerated": true, + "name": "Convert", + "phase": "Conversion", + "parameters": [], + "toggle": { + "autoGenerated": true, + "name": "Convert to ONNX format", + "type": "bool", + "path": "passes.optimum_convert", + "actions": [ + [], + [] + ], + "readOnly": true + } + }, + { + "name": "Quantize", + "phase": "Quantization", + "parameters": [ + { + "name": "Quantization Dataset", + "tags": [ + "QuantizationDataset" + ], + "type": "enum", + "path": "data_configs[0].load_dataset_config.data_name", + "values": [ + "wikipedia" + ], + "template": { + "path": "data_configs[0].load_dataset_config.data_name", + "values": [ + "wikipedia" + ], + "template": "QuantizationDataset" + } + }, + { + "name": "Quantization Dataset Split", + "tags": [ + "QuantizationDatasetSplit", + "DependsOnDataset" + ], + "type": "enum", + "path": "data_configs[0].load_dataset_config.split", + "values": [ + "train", + "validation", + "test" + ], + "template": { + "path": "data_configs[0].load_dataset_config.split", + "template": "QuantizationDatasetSplit" + } + }, + { + "name": "Quantization Dataset Size", + "type": "int", + "path": "data_configs[0].load_dataset_config.max_samples", + "template": { + "path": "data_configs[0].load_dataset_config.max_samples", + "template": "QuantizationDatasetSize" + } + } + ], + "toggle": { + "autoGenerated": true, + "name": "Quantize model", + "type": "bool", + "path": "passes.optimum_convert", + "actions": [ + [], + [] + ], + "readOnly": true + } + }, + { + "name": "Evaluate", + "phase": "Evaluation", + "parameters": [], + "toggle": { + "autoGenerated": true, + "name": "Evaluate model performance", + "type": "bool", + "path": "evaluator", + "actions": [ + [], + [ + { + "type": "delete", + "path": "evaluator" + } + ] + ] + } + } + ] +} diff --git a/baai-bge-small-en-v1.5/aitk/bge-small-en-v1.5_qdq_amd.json b/baai-bge-small-en-v1.5/aitk/bge-small-en-v1.5_qdq_amd.json new file mode 100644 index 00000000..a40f3446 --- /dev/null +++ b/baai-bge-small-en-v1.5/aitk/bge-small-en-v1.5_qdq_amd.json @@ -0,0 +1,230 @@ +{ + "input_model": { + "type": "HfModel", + "model_path": "BAAI/bge-small-en-v1.5", + "task": "feature-extraction", + "io_config": { + "input_names": [ + "input_ids", + "attention_mask", + "token_type_ids" + ], + 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"input_cols": [ + "premise" + ], + "padding": "max_length", + "max_length": 128, + "max_samples": 10 + }, + "dataloader_config": { + "batch_size": 1 + } + } + ], + "evaluators": { + "common_evaluator": { + "metrics": [ + { + "name": "latency", + "type": "latency", + "data_config": "evaluation_data_config", + "sub_types": [ + { + "name": "avg", + "priority": 2, + "goal": { + "type": "percent-min-improvement", + "value": 0.1 + } + }, + { "name": "p50", "metric_config": { "warmup_num": 20, "repeat_test_num": 100 } }, + { "name": "p75", "metric_config": { "warmup_num": 20, "repeat_test_num": 100 } }, + { "name": "p90", "metric_config": { "warmup_num": 20, "repeat_test_num": 100 } }, + { "name": "p95", "metric_config": { "warmup_num": 20, "repeat_test_num": 100 } }, + { "name": "p99", "metric_config": { "warmup_num": 20, "repeat_test_num": 100 } }, + { "name": "min", "metric_config": { "warmup_num": 20, "repeat_test_num": 100 } }, + { "name": "max", "metric_config": { "warmup_num": 20, 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a/baai-bge-small-en-v1.5/aitk/bge-small-en-v1.5_qdq_qnn.json.config b/baai-bge-small-en-v1.5/aitk/bge-small-en-v1.5_qdq_qnn.json.config new file mode 100644 index 00000000..071ab0b3 --- /dev/null +++ b/baai-bge-small-en-v1.5/aitk/bge-small-en-v1.5_qdq_qnn.json.config @@ -0,0 +1,231 @@ +{ + "name": "Convert to Qualcomm NPU", + "oliveFile": "bge/bge-small-en-v1.5_ptq_qnn.json", + "runtime": { + "autoGenerated": true, + "name": "Evaluate on", + "type": "enum", + "displayNames": [ + "Qualcomm NPU", + "CPU" + ], + "path": "systems.qnn_system.accelerators.0.execution_providers.0", + "values": [ + "QNNExecutionProvider", + "CPUExecutionProvider" + ], + "readOnly": false + }, + "sections": [ + { + "autoGenerated": true, + "name": "Convert", + "phase": "Conversion", + "parameters": [], + "toggle": { + "autoGenerated": true, + "name": "Convert to ONNX format", + "type": "bool", + "path": "passes.conversion", + "actions": [ + [], + [] + ], + "readOnly": true + } + }, + { + "name": "Quantize", + 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+ "template": "EvaluationDataset" + } + }, + { + "name": "Evaluation Dataset Split", + "tags": [ + "EvaluationDatasetSplit", + "DependsOnDataset" + ], + "type": "enum", + "path": "data_configs[1].load_dataset_config.split", + "values": [ + "train", + "validation", + "test" + ], + "template": { + "path": "data_configs[1].load_dataset_config.split", + "template": "EvaluationDatasetSplit" + } + }, + { + "name": "Quantization Dataset Size", + "type": "int", + "path": "data_configs[1].pre_process_data_config.max_samples", + "template": { + "path": "data_configs[1].pre_process_data_config.max_samples", + "template": "QuantizationDatasetSize" + } + } + ], + "toggle": { + "autoGenerated": true, + "name": "Evaluate model performance", + "type": "bool", + "path": "evaluator", + "actions": [ + [], + [ + { + "type": "delete", + "path": "evaluator" + } + ] + ] + } + } + ] +} diff --git a/baai-bge-small-en-v1.5/aitk/inference_sample.ipynb b/baai-bge-small-en-v1.5/aitk/inference_sample.ipynb new file mode 100644 index 00000000..8120cbc3 --- /dev/null +++ b/baai-bge-small-en-v1.5/aitk/inference_sample.ipynb @@ -0,0 +1,150 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "onnx_model_path = \"./model/model.onnx\"\n", + "\n", + "ExecutionProvider=\"QNNExecutionProvider\"\n", + "if ExecutionProvider == \"OpenVINOExecutionProvider\":\n", + " onnx_model_path = \"./model/openvino_model_st_quant.onnx\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inputs = \"This is an example sentence.\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import onnxruntime as ort\n", + "import torch\n", + "import torch.nn.functional as F\n", + "\n", + "from transformers import AutoModel, AutoTokenizer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def mean_pooling(model_output, attention_mask):\n", + " token_embeddings = torch.tensor(model_output[0])\n", + " input_mask_expanded = attention_mask.unsqueeze(-1).expand_as(token_embeddings).float()\n", + " return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-small-en-v1.5')\n", + "encoded_input = tokenizer(\n", + " inputs,\n", + " padding=\"max_length\",\n", + " max_length=128,\n", + " truncation=True,\n", + " add_special_tokens=True,\n", + " return_tensors=\"pt\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def add_ep_for_device(session_options, ep_name, device_type, ep_options=None):\n", + " ep_devices = ort.get_ep_devices()\n", + " for ep_device in ep_devices:\n", + " if ep_device.ep_name == ep_name and ep_device.device.type == device_type:\n", + " print(f\"Adding {ep_name} for {device_type}\")\n", + " session_options.add_provider_for_devices([ep_device], {} if ep_options is None else ep_options)\n", + "\n", + "\n", + "session_options = ort.SessionOptions()\n", + "\n", + "add_ep_for_device(session_options, ExecutionProvider, ort.OrtHardwareDeviceType.NPU)\n", + "\n", + "session = ort.InferenceSession(\n", + " onnx_model_path, # a model wirh QNN EPContext nodes\n", + " sess_options=session_options,\n", + ")\n", + "\n", + "input_ids = encoded_input[\"input_ids\"]\n", + "attention_mask = encoded_input[\"attention_mask\"]\n", + "token_type_ids = encoded_input[\"token_type_ids\"]\n", + "inputs = {\n", + " \"input_ids\": input_ids.long().cpu().numpy(),\n", + " \"attention_mask\": attention_mask.long().cpu().numpy(),\n", + " \"token_type_ids\": token_type_ids.long().cpu().numpy()\n", + "}\n", + "\n", + "outputs = session.run(None, inputs)\n", + "embeds_1 = mean_pooling(outputs, encoded_input['attention_mask'])\n", + "embeds_1 = F.normalize(embeds_1, p=2, dim=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# get text embedding from orinal model, as ground truth.\n", + "model = AutoModel.from_pretrained('BAAI/bge-small-en-v1.5').eval()\n", + "with torch.no_grad():\n", + " outputs = model(**encoded_input)\n", + " embeds_2 = mean_pooling(outputs, encoded_input['attention_mask'])\n", + " embeds_2 = F.normalize(embeds_2, p=2, dim=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "similarity = F.cosine_similarity(embeds_1, embeds_2).item()\n", + "print(\"Similarity: \", similarity)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/baai-bge-small-en-v1.5/aitk/info.yml b/baai-bge-small-en-v1.5/aitk/info.yml new file mode 100644 index 00000000..fc040a82 --- /dev/null +++ b/baai-bge-small-en-v1.5/aitk/info.yml @@ -0,0 +1,20 @@ +keywords: + aitk +arch: bert +recipes: + - file: "bge-small-en-v1.5_qdq_qnn.json" + device: npu + ep: QNNExecutionProvider + - file: "bge-small-en-v1.5_qdq_amd.json" + device: npu + ep: VitisAIExecutionProvider + - file: "bge-small-en-v1.5_context_ov_static.json" + devices: + - npu + - cpu + - gpu + ep: OpenVINOExecutionProvider +aitk: + modelInfo: + id: "huggingface/BAAI/bge-small-en-v1.5" + version: 1 diff --git a/baai-bge-small-en-v1.5/aitk/model_project.config b/baai-bge-small-en-v1.5/aitk/model_project.config new file mode 100644 index 00000000..05101c4e --- /dev/null +++ b/baai-bge-small-en-v1.5/aitk/model_project.config @@ -0,0 +1,20 @@ +{ + "workflows": [ + { + "file": "bge-small-en-v1.5_qdq_qnn.json", + "templateName": "bge-small-en-v1.5_qdq_qnn" + }, + { + "file": "bge-small-en-v1.5_qdq_amd.json", + "templateName": "bge-small-en-v1.5_qdq_amd" + }, + { + "file": "bge-small-en-v1.5_context_ov_static.json", + "templateName": "bge-small-en-v1.5_context_ov_static" + } + ], + "modelInfo": { + "id": "huggingface/BAAI/bge-small-en-v1.5", + "version": 1 + } +} \ No newline at end of file diff --git a/baai-bge-small-en-v1.5/aitk/requirements.txt b/baai-bge-small-en-v1.5/aitk/requirements.txt new file mode 100644 index 00000000..c1df102f --- /dev/null +++ b/baai-bge-small-en-v1.5/aitk/requirements.txt @@ -0,0 +1,5 @@ +olive-ai +datasets +optimum +mteb +polars-lts-cpu \ No newline at end of file diff --git a/baai-bge-small-en-v1.5/aitk/user_script.py b/baai-bge-small-en-v1.5/aitk/user_script.py new file mode 100644 index 00000000..5530cb2d --- /dev/null +++ b/baai-bge-small-en-v1.5/aitk/user_script.py @@ -0,0 +1,143 @@ +# ------------------------------------------------------------------------- +# Copyright (c) Intel Corporation. All rights reserved. +# Licensed under the MIT License. +# -------------------------------------------------------------------------- +import datasets +import mteb +import numpy as np +import torch +from transformers import AutoTokenizer + +from olive.constants import Framework +from olive.data.registry import Registry +from olive.model import OliveModelHandler + +# ------------------------------------------------------------------------- +# Common Dataset +# ------------------------------------------------------------------------- + +seed = 0 +# seed everything to 0 for reproducibility, https://pytorch.org/docs/stable/notes/randomness.html +# do not set random seed and np.random.seed for aml test, since it will cause aml job name conflict +torch.manual_seed(seed) +# the following are needed only for GPU +torch.cuda.manual_seed(seed) +torch.backends.cudnn.deterministic = True +torch.backends.cudnn.benchmark = False + +# set max sequence length +MAX_SEQ_LENGTH = 128 + +# define the tokenizer for BGE model +tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-small-en-v1.5") +VOCAB_SIZE = len(tokenizer) + +# set default input +default_input = torch.ones(1, MAX_SEQ_LENGTH, dtype=torch.int64) + +# define model inputs +model_inputs = { + "input_ids": default_input, + "attention_mask": default_input, + "token_type_ids": default_input, +} + +# capture input names +INPUT_NAMES = list(model_inputs) + + +class OliveEncoder: + def __init__(self, model, session): + self.model = model + self.session = session + self.tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-small-en-v1.5") + + def encode(self, corpus: list, **kwargs): + model_output = None + if self.model.framework == Framework.ONNX: + encoded_input = self.tokenizer( + corpus, padding="max_length", max_length=MAX_SEQ_LENGTH, truncation=True, return_tensors="np" + ) + # batch_size is 1 for static model + model_outputs = [] + for i in range(len(corpus)): + model_inputs = { + "input_ids": encoded_input.input_ids[i : i + 1, :].astype(np.int64), + "attention_mask": encoded_input.attention_mask[i : i + 1, :].astype(np.int64), + "token_type_ids": encoded_input.token_type_ids[i : i + 1, :].astype(np.int64), + } + model_output = self.model.run_session(self.session, model_inputs)[0] + model_outputs.append(model_output[0]) + model_output = np.array(model_outputs) + elif self.model.framework == Framework.PYTORCH: + encoded_input = self.tokenizer(corpus, padding=True, truncation=True, return_tensors="pt") + model_inputs = { + "input_ids": encoded_input.input_ids, + "attention_mask": encoded_input.attention_mask, + "token_type_ids": encoded_input.token_type_ids, + } + with torch.no_grad(): + model_output = self.model.run_session(self.session, model_inputs) + model_output = model_output.last_hidden_state.numpy() + # select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding. + return model_output[:, 0, :] + + +def eval_accuracy(model: OliveModelHandler, device, execution_providers, tasks=None): + """Evaluate accuracy using MTEB (Massive Text Embedding Benchmark) for standardized evaluation.""" + sess = model.prepare_session(inference_settings=None, device=device, execution_providers=execution_providers) + + # Use default tasks if none provided + if tasks is None: + tasks = ["Banking77Classification"] # Default to Banking77 for BGE model evaluation + + evaluation = mteb.MTEB(tasks=tasks) + olive_encoder = OliveEncoder(model, sess) + results = evaluation.run(olive_encoder, output_folder=None) + + # Return the main score from the first task + return results[0].scores["test"][0]["main_score"] + + +@Registry.register_dataset() +def bge_small_en_dataset(data_name, split, max_samples): + # load the raw wikipedia dataset for tuning. Load just 300 examples for speed. + raw_dataset = datasets.load_dataset(data_name, "20220301.en", split=split, trust_remote_code=True) + + # Apply max_samples limit after loading + if max_samples: + raw_dataset = raw_dataset.select(range(min(max_samples, len(raw_dataset)))) + + def _preprocess_fn(examples): + return tokenizer( + examples["text"], + padding="max_length", + max_length=MAX_SEQ_LENGTH, + truncation=True, + ) + + # preprocess the dataset + return raw_dataset.map(_preprocess_fn, batched=True, batch_size=1) + + +def custom_transform_func(data_item): + return { + name: np.asarray([np.array([g.flatten() for g in data_item[name]]).flatten()], dtype=np.int64) + for name in INPUT_NAMES + } + + +def custom_example_func(): + vocab_size = VOCAB_SIZE + batch_size = 1 + sequence_length = MAX_SEQ_LENGTH + + input_ids = torch.randint(0, vocab_size, (batch_size, sequence_length)) + + # Generate random attention_mask (1s for actual tokens, 0s for padding) + attention_mask = default_input + + # Generate random token_type_ids (0 for sentence 1, 1 for sentence 2) + token_type_ids = default_input + + return [input_ids, attention_mask, token_type_ids] \ No newline at end of file